Categorizing Intelligent Lessons Learned Systems

Lessons learned systems are knowledge management solutions that serve the purpose of capturing, storing, disseminating and sharing an organization’s verified lessons. In this paper we propose a two-step categorization method to support he design of intelligent lessons learned systems. The first step refers to the categories of the lessons learned processes the system is designed to support. The second step refers to the categories of the system itself. These categories &-e based on systems available online and described in the literature. We conclude by summarizing representational nd other important issues that need to be addressed when designing intelligent lessons learned systems. Motivation and definition Lessons learned (LL) systems have been deployed many military, commercial, and government organizations to disseminate validated experiential lessons.’ They support organizational lessons learned processes, which use a knowledge management (KM) approach to collect, store, disseminate, and reuse experiential working knowledge that, when applied, can significantly benefit targeted organizational processes (Davenport & Prusak, 1998). Unfortunately, based on our interviews and discussions with members of several LL centers (e.g., at the Joint Warfighting Center, the Department of Energy (DOE), the Naval Facilities Engineering Command, Goddard Space Flight Center (NASA), the Construction Industry Institute), we learned that LL systems, although well-intentioned, are rarely used. Our goal is to design, develop, evaluate, and deploy LL systems that support knowledge sharing. In this paper, we categorize LL systems and identify some pertinent research directions that may benefit from applying artificial intelligence (AI) techniques. Lessons learned were originally conceived of as guidelines, tips, or checklists of what went right or wrong in a particular event (Stewart, 1997); the Canadian Army Lessons Learned Center and the Secretary of the Army for Research, Development, and Acquisition, among others, still use this notion. Today, this concept has evolved because organizations working towards improving the Our WWW page, www.aic.nrl.navv.mil/-aha/lessons, contains additional information the organizations mentioned in this paper. results obtained from LL systems have adopted binding criteria (e.g., lessons have to be validated for correctness and should impact organizational behavior). This definition is now used by the American, European, and Japanese Space agencies: A lesson learned is knowledge or understanding gained by experience. The experience may be positive, as in a successful test or mission, or negative, as in a mishap or failure...A lesson must be significant in that it has a real or assumed impact on operations; valid in that is factually and technically correct; and applicable in that it identifies a specific design, process, or decision that reduces or eliminates the potential for failures and mishaps, or reinforces a positive result (Secchi, 1999). Lessons learned, as well as other KM artifacts, are usually described with respect to their origin, application, and results. Table 1 contrasts artifacts of frequent interest in KM strategies. Table 1. Contrasting knowledge management artifacts. Originates from Experiences? Yes